Elucidating the mechanisms of protein secretion across the outer membrane by bacterial autotransporters

Project Summary/Abstract Pathogenic Gram-negative bacteria are collectively responsible for over 5 million deaths annually. A number of species also exhibit high levels of antibiotic resistance, comprising nine out of twelve members on the World Health Organization’s list of priority pathogens. Gram-negative bacterial infection is often mediated by so-called autotransporters, a class of proteins that cross the outer membrane to the extracellular space where they act as virulence factors, such as adhesins, proteases, and other harmful agents. Autotransporters consist of a translocator domain, which remains in the membrane, and a passenger domain, which secretes across the membrane to the other side, even without the use chemical energy, e.g., ATP. Targeting these autotransporters for inhibition represents a promising means of controlling infection while limiting the development of resistance. However, first, research into the molecular mechanisms of autotransporter folding, secretion, and expression beyond the cell surface is critically needed. This project will meet that need through three specific aims. In the first aim, how the passenger domain folds will be characterized, answering why folding in vivo is orders of magnitude faster than in vitro. The second aim focuses on the secretion of the passenger domain across the membrane through a hybrid-β-barrel of the translocator domain with BamA, the protein responsible for its membrane insertion. The pathway through the combined barrels will be determined, as well as the influence of the outer membrane on the process. In the third aim, another class of autotransporters, two-partner secretion systems, will be modeled, with a goal of identifying both commonalities and differences between them and other classes. The primary methodological tool to be used for this project is atomic-scale molecular dynamics (MD) simulations. These simulations will all be carried out in realistic environments, including an accurate model of the asymmetric Gram-negative outer membrane. Multiple innovative approaches will also be used, including deep learning for modeling the folding process, Markov state modeling for extracting kinetics information, and coarse- grained Brownian dynamics for observing spontaneous secretion. Close collaboration with multiple experimental labs will provide key inputs to and validation of the MD simulation results.